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babffc8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 | from __future__ import annotations
import argparse
import sys
from pathlib import Path
import torch
sys.path.append(str(Path(__file__).resolve().parents[1]))
from src.io_utils import apply_overrides, load_config
from src.qwen_video_utils import generate_one, load_model_for_training, load_processor
from src.train_utils import oom_help, quantization_config_from_config
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("--config", required=True)
parser.add_argument("--video", required=True)
parser.add_argument("--model_name", default=None)
parser.add_argument("--prompt", default=None)
parser.add_argument("--max_frames", type=int, default=None)
parser.add_argument("--video_fps", type=float, default=None)
args = parser.parse_args()
cfg = apply_overrides(load_config(args.config), model_name=args.model_name)
processor = load_processor(cfg["model_name"])
model = load_model_for_training(
cfg["model_name"],
quantization_config=quantization_config_from_config(cfg),
device_map="auto",
dtype="bfloat16" if cfg.get("bf16", True) else "float16",
)
model.eval()
try:
prediction = generate_one(
model,
processor,
args.video,
args.prompt or cfg["prompt"],
cfg.get("generation", {}),
video_fps=args.video_fps if args.video_fps is not None else cfg.get("video_fps"),
max_frames=args.max_frames if args.max_frames is not None else cfg.get("max_frames"),
)
except torch.cuda.OutOfMemoryError as exc:
raise RuntimeError(oom_help()) from exc
print(prediction)
if __name__ == "__main__":
main()
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